Data Analysis and Artificial Intelligence

Course overview

Goals of the Training The focus of the Data Analysis and Artificial Intelligence training is to impart knowledge and skills in the areas of Data Science and Artificial Intelligence (AI). The course begins with an introduction to Data Science, covering fundamental concepts and techniques. Advanced data analysis is addressed to develop deeper analytical skills. Data Analytics and Big Data are central components that illuminate the analysis of large data sets and their significance for businesses. Data analysis projects provide the opportunity to connect theoretical content from the various subject areas. The course also includes the basics of Artificial Intelligence (AI), with particular emphasis on generative AI. The project "AI Fluency - Introduction to Generative AI" allows for further exploration of current developments in the field of AI. The training requires prior knowledge of Python, common machine learning libraries, and data analysis. In summary, it provides a solid foundation for understanding and applying data analysis and AI techniques. Contents of the Training Introduction to Data Science Introduction to Data Science Data Data Science in Business Statistics Machine Learning Advanced Data Analysis Analysis of Business Performance Text Mining Web and Social Media Analysis Experimentation and Testing Data Analytics and Big Data Introduction to Data Analysis Statistical Fundamentals Data Mining Big Data Methods and Technologies Legal Aspects of Data Analysis Solution Scenarios Application of Big Data in Industry Project: Data Analysis Transfer of methodological knowledge to implement analytical use cases in the real world from the aforementioned problem areas. Artificial Intelligence History of AI Modern AI Systems Reinforcement Learning Natural Language Processing (NLP) Computer Vision Project: AI Fluency - Introduction to Generative AI The course provides competent classification and selection of AI tools and demonstrates, among other things, how to develop professional prompts using the R.C.C.E. method. Students learn to critically evaluate AI results, effectively integrate human-AI collaboration into workflows, and use AI responsibly and reflectively.

Upcoming dates

• 2026-09-30T22:00:00.000Z · 2027-03-31T22:00:00.000Z · Karlsruhe, Baden · Blended Learning
• 2026-09-30T22:00:00.000Z · 2027-03-31T22:00:00.000Z · Nürnberg, Mittelfranken · Blended Learning
• 2026-09-30T22:00:00.000Z · 2027-03-31T22:00:00.000Z · München · Blended Learning
• 2026-09-30T22:00:00.000Z · 2027-03-31T22:00:00.000Z · Leipzig · Blended Learning
• 2026-09-30T22:00:00.000Z · 2027-03-31T22:00:00.000Z · Essen, Ruhr · Blended Learning